International audienceThis paper presents the SPIN micro-network that is a generic, scalable interconnect architecture for system on chip. The SPIN architecture relies on packet switching and point-to-point bi-directional links between the routers implementing the micro-network. SPIN gives the system designer the simple view of a single shared address space and provides a variable number of VCI compliant communication interfaces for both initiators (masters) and targets (slaves). Performance comparisons between a classical PI-bus based interconnect and the SPIN micro-network are analyzed

Hyperspectral images contain tens to hundreds of bands, implying a high spectral resolution. This high spectral resolution allows for obtaining a precise signature of structures and compounds that make up the captured scene. Among the types of processing that may be applied to Hyperspectral Images, classification using machine learning models stands out. The classification process is one of the most relevant steps for this type of image. It can extract information using spatial and spectral information and spatial-spectral fusion. Artificial Neural Network models have been gaining prominence among existing classification techniques. They can be applied to data with one, two, or three dimensions. Given the above, this work evaluates Convolutional Neural Network models with one, two, and three dimensions to identify the impact of classifying Hyperspectral Images with different types of convolution. We also expand the comparison to Recurrent Neural Network models, Attention Mechanism, and the Transformer architecture.. Furthermore, a novelty pre-processing method is proposed for the classification process to avoid generating data leaks between training, validation, and testing data. The results demonstrated that using 1 Dimension Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and Transformer architectures reduces memory consumption and sample processing time and maintain a satisfactory classification performance up to 99% accuracy on larger datasets. In addition, the Transfomer architecture can approach the 2D-CNN and 3D-CNN architectures in accuracy using only spectral information. The results also show that using two or three dimensions convolution layers improves accuracy at the cost of greater memory consumption and processing time per sample. Furthermore, the pre-processing methodology guarantees the disassociation of training and testing data.

RISC-V is a novel open instruction set architecture that supports multiple platforms while maintaining simplicity and reliability. Despite its novelty, the software support for RISC-V has been increasing in the last years, given that popular toolchains and operating systems already have support for RISC-V. However, although many works have been exploring the RISC-V software ecosystem, no work that raised the current state of software support for RISC-V is available. In this context, this survey reviews the contributions introduced in the last years to understand the RISC-V’s software ecosystem and its usage in both academic and industrial environments. We classified and evaluated the works into four main categories: application fields, RISC-V implementations, software architecture, and deployment features. The primary goal of this research is to provide the community with a comprehensive overview of the current state of RISC-V software support and identify and highlight the main contributions from recent work.